With the popularity of wireless devices and the increasing demand of network applications, it is emergent to develop more effective communications paradigm to enable new and powerful pervasive applications, and to allow services to be accessed anywhere, at anytime. However, it is extremely challenging to construct efficient and reliable networks to connect wireless devices due to the increasing communications need and the dynamic nature of wireless communications. In order to improve transmission throughput, many efforts have been made in recent years to reduce traffic and hence transmission collisions by constructing backbone networks with the minimum size. However, many other important issues need to be considered. Instead of simply minimizing the number of backbone nodes or supporting some isolated network features, in this work, we exploit the use of algebraic connectivity to control backbone network topology design for concurrent improvement of backbone network robustness, capacity, stability and routing efficiency. In order to capture other network features, we provide a general cost function and introduce a new metric, connectivity efficiency, to trade off algebraic connectivity and cost for backbone construction. We formally prove the problem of formulating a backbone network with the maximum connectivity efficiency that is NP-hard, and design both centralized and distributed algorithms to build more robust and efficient backbone infrastructure to better support the application needs. We have made extensive simulations to evaluate the performance of our work. Compared to literature studies on constructing wireless backbone networks, the incorporation of algebraic connectivity into the network performance metric could achieve much higher throughput and delivery ratio, and much lower end-to-end delay and routing distances under all test scenarios. We hope our work could stimulate more future research in designing more reliable and efficient networks. Our performance studies demonstrate that, compared to peer work, the incorporation of algebraic connectivity into network performance metric could achieve much higher throughput and delivery ratio, and much lower end-to-end delay and routing distances under all test scenarios. We hope our work could stimulate more future research in designing more reliable and efficient networks.

With the popularity of wireless devices and the increase of computing and storage resources, there are increasing interests in supporting mobile computing techniques. Particularly, ad hoc networks can potentially connect different wireless devices to enable more powerful wireless applications and mobile computing capabilities. To meet the ever increasing communication need, it is important to improve the network throughput while guaranteeing transmission reliability. Multiple-input-multiple-output (MIMO) technology can provide significantly higher data rate in ad hoc networks where nodes are equipped with multiantenna arrays. Although MIMO technique itself can support diversity transmission when channel condition degrades, the use of diversity transmission often compromises the multiplexing gain and is also not enough to deal with extremely weak channel. Instead, in this work, we exploit the use of cooperative relay transmission (which is often used in a single antenna environment to improve reliability) in a MIMO-based ad hoc network to cope with harsh channel condition. We design both centralized and distributed scheduling algorithms to support adaptive use of cooperative relay transmission when the direct transmission cannot be successfully performed. Our algorithm effectively exploits the cooperative multiplexing gain and cooperative diversity gain to achieve higher data rate and higher reliability under various channel conditions. Our scheduling scheme can efficiently invoke relay transmission without introducing significant signaling overhead as conventional relay schemes, and seamlessly integrate relay transmission with multiplexed MIMO transmission. We also design a MAC protocol to implement the distributed algorithm. Our performance results demonstrate that the use of cooperative relay in a MIMO framework could bring in a significant throughput improvement in all the scenarios studied, with the variation of node density, link failure ratio, packet arrival rate, and retransmission threshold.

Wireless visual sensor networks (WVSNs) can not only provide monitoring functions like wireless sensor networks but also capture images of the monitored area. This is why the barrier coverage problem in WVSNs has received attention from many researchers. However, previous research of barrier coverage did not consider breadth of coverage, i.e., the width of collected images. In this paper, we consider breadth to increase the quality of monitor (QoM) of WVSNs. For WVSNs consisting of camera sensors without rotation capability, we propose an algorithm called basic distributed β-breadth belt-barrier construction algorithm without rotation (D-TriB). D-TriB constructs a belt-barrier with β breadth to offer β level of QoM, we call β-QoM. For WVSNs consisting of camera sensors with rotation capability, we propose the enhanced distributed β -breadth belt-barrier construction algorithm with rotation (D-TriBR). The proposed algorithms can not only reduce the number of camera sensors required to construct a barrier but also ensure that any barrier with β-QoM in the network can be identified. Finally, the successful rate of the proposed algorithms under different conditions, including β requirement, sensor distribution, and rotation capability, is also evaluated through simulations.

Wireless sensor networks have been used in a wide variety of applications. Recently, networks consisting of directional sensors have gained prominence. An important challenge facing directional sensor networks (DSNs) is maximizing the network lifetime while covering all the targets in an area. One effective method for saving the sensors’ energy and extending the network lifetime is to partition the DSN into several covers, each of which can cover all targets, and then to activate these covers successively. This paper first proposes a fully distributed algorithm based on irregular cellular learning automata to find a near-optimal solution for selecting each sensor’s appropriate working direction. Then, to find a near-optimal solution that can cover all targets with the minimum number of active sensors, a centralized approximation algorithm is proposed based on distributed learning automata. This algorithm takes advantage of learning automata (LA) to determine the sensors that must be activated at each stage. As the presented algorithm proceeds, the activation process is focused on the sensor nodes that constitute the cover set with the minimum number of active sensors. Through simulations, we indicate that the scheduling algorithm based on LA has better performance than the greedy algorithm-based scheme in terms of maximizing network lifetime.

Wireless sensor networks are generally composed of a large number of hardware devices of the same type, deployed over a region of interest in order to perform a monitoring activity on a set of target points. Nowadays, several different types of sensor devices exist, which are able to monitor different aspects of the region of interest (including sound, vibrations, proximity, chemical contaminants, among others) and may be deployed together in a heterogeneous network. In this work, we face the problem of maximizing the amount of time during which such a network can remain operational, while maintaining at all times a minimum coverage guarantee for all the different sensor types. Some global regularity conditions in order to guarantee a fair level of coverage for each sensor type to each target are also taken into account in a second variant of the proposed problem. For both problem variants we developed an exact approach, which is based on a column generation algorithm whose subproblem is either solved heuristically by means of a genetic algorithm or optimally by an appropriate ILP formulation. In our computational tests the proposed genetic algorithm is shown to be able to dramatically speed up the procedure, enabling the resolution of large-scale instances within reasonable computational times.

Wireless multimedia sensors have been frequently used for detecting events in acoustic rich environments such as protected area networks. Such areas have diverse habitat, frequently varying terrain and are a source of very large number of acoustic events. This work is aimed at detecting the tree cutting event in a forest area, by identifying the acoustic pattern generated due to an axe hitting a tree bole, with the help of wireless multimedia sensors. A series of operations using the hamming window, wiener filter, Otsu thresholding and mathematical morphology are used for removing the unwanted clutter from the spectrogram obtained from such events. Using the sparse nature of the acoustic signals, a compressed sensing based energy efficient data gathering scheme is devised for accurate event reporting. A network of Mica2 motes is deployed in a real forest area to test the validity of the proposed scheme. Analytical and experimental results proves the efficacy of the proposed event detection scheme.

Wireless Sensor Networks (WSNs) have acquired new features recently, i.e., both the sensor and the antenna of a node can be directional. This brings new challenges to the Connected Coverage (CoCo) problem, where a finite set of targets needs to be monitored by some active sensor nodes, and the connectivity of these active nodes with the sink must be retained at the same time. In this paper, we study the Minimum-Energy Connected Coverage (MeCoCo) problem in WSNs with Omni-directional (O) and Directional (D) features, aiming at minimizing the total energy cost of both sensing and connectivity. Considering different combinations of O and D features, we study the MeCoCo problem under four cases, namely: O-Antenna and O-Sensor (OAOS), O-Antenna and D-Sensor (OADS), D-Antenna and D-Sensor (DADS), as well as D-Antenna and O-Sensor (DAOS). We prove that the MeCoCo problem is NP-hard under all these cases, and present approximation algorithms with provable approximation ratios. In particular, we propose a constant-approximation for OAOS, and polylogarithmic approximations for all other cases. Finally, we conduct extensive simulations and the results strongly confirm the effectiveness of our approach.

Wireless Sensor Network (WSN) is an emerging technology that is gaining much importance owing to its immense contribution in various day-to-day applications. A sensor is battery-operated, unattended low-cost device with limited computing, communication and storage capabilities. Thus the network lifetime has become the key characteristic for evaluating sensor networks in an application-specific way. There are certain approaches in literature which consider the lifetime maximization problem. However, they suffer from impulsive energy hole, coverage hole and communication hole. In this paper we propose a novel Energy Efficient Connected Coverage (EECC) scheduling to maximize the lifetime of the WSN. The EECC adheres to Quality of Service (QoS) metrics such as remaining energy, coverage and connectivity. In EECC the sensor which doesn't contribute to coverage will act as a relay node to reduce the burden of the sensing node. The sensing node senses the target whereas the relaying node communicates the sensory information to the sink. The EECC forms non-disjoint cover sets using remaining energy, coverage and connectivity of every sensor. The proposed EECC outperforms similar scheduling algorithms found in the literature in an energy efficient way with the short execution time. Through simulations the constancy of EECC in extending the lifetime of WSN is confirmed.

Visual sensor networks normally consist of a collection of camera sensors deployed randomly yet densely to fully cover a set of targets. Due to high redundancy incurred, it is possible to both preserve energy and enhance coverage quality by first switching off some sensors and then adjusting the orientations of the remaining ones. The problem is that no global knowledge of the environment is available to be used to decide which sensors should be switched off and which ones should adjust their orientations. In this paper, we propose a new distributed game theoretic approach to full target coverage. A potential game is formulated in which a utility function is designed to consider the tradeoff between coverage quality and energy consumption. In order to solve the game, we present a distributed payoff-based learning algorithm where each sensor has only access to its last two actions played and own utility values. Simulation results show that our proposed game-theoretic approach has greater energy efficiency and can extend the network lifetime, as compared with prior approaches.

Visual infrastructure, which consists of connected visual sensors, has been extensively deployed and is vital for various important applications, such as surveillance, tracking, and monitoring. However, there are still many problems regarding visual sensor deployment for optimal coverage and visual data processing technology. Challenges remain with the sectoral visual sensing model, the complexity of image processing, and these sensors’ vulnerability to noisy environments. Solving these problems will improve the performance of visual infrastructure, which increases accuracy and efficiency for these applications. This dissertation focuses on visual-infrastructurerelated technologies. In particular, we study the following problems. First, we study visual infrastructure deployment. We propose local face-view barrier coverage (L-Faceview), a novel concept that achieves statistical barrier coverage in visual sensor networks leveraging mobile objects’ trajectory information. We derive a rigorous probability bound for this coverage via a feasible deployment pattern. The proposed detection probability bound and deployment pattern can guide practical camera sensor deployments in visual infrastructure with limited budgets. Second, we study visual-infrastructure-based object recognition. We design and implement R-Focus, a platform with visual sensors that detects and verifies a person holding a mobile phone nearby with the assistance of electronic sensors. R-Focus performs visual and electronic data collection and rotates based on the collected data.

Unlike convectional omnidirectional sensors that consistently have an omniangle of detecting range, directional sensors may have a restricted point of detecting range because of specialized requirements or cost considerations. A directional sensor system comprises of various directional sensors, which can switch to several directions to broaden their detecting capacity to cover every one of the objectives in a given territory. Power preservation is still a significant issue in such directional sensor networks. In this paper, we consider the multiple directional cover sets (MDCS) problem of organizing the directions of sensors into a group of non-disjoint cover sets to extend the network lifetime. It is an NP-complete problem. Firstly, a new model of MDCS is introduced in the form of Mixed Binary Integer Linear Programming (MBILP). Secondly, we investigate a new method based on DC programming and DC algorithm (DCA) for solving MDCS. Numerical results are presented to demonstrate the performance of the algorithm.

Traditional sensor nodes are powered by batteries. The limited battery capacity, however, constrains the lifetime of the wireless sensor networks. Wireless power transfer technology allows energy transfers from a charger to sensor nodes via wireless, and thus solves the problem completely. One fundamental issue in wireless rechargeable sensor networks is the wireless charger placement ∗ 基金项目 : 国家重点基础研究发展计划 (973)(2012CB316201, 2014CB340303); 国家自然科学基金 (61133006, 61321491, 61373130, 61170247, 613230428); 江苏省研究生培养创新工程(CXZZ12_0056) 收稿时间: 2013-10-24; 定稿时间: 2014-04-03 1712 Journal of Software 软件学报 Vol.26, No.7, July 2015 problem, i.e., how to effectively place the chargers to maximize the overall charging utility of the network. Existing works mainly focus on the deployment issues of omnidirectional chargers, which are confined to positions such as the end point of triangles or lattice point in a grid. These works inevitably have their limitations. This study is to consider the general placement problem in which the charging area of chargers is a sector and the charger can be deployed at any position in the field with arbitrary orientation. First, a charging model for directional chargers is constructed based on trace data. Then, a series of novel techniques is proposed to transform the problem to develop an effective algorithm, CDG (charger deployment-greedy), with approximation ratio (1−1/e)/(1+e) to solve this problem. The simulation results demonstrate the effectiveness of the CDG algorithm. Compared with other two random algorithms, the CDG algorithm has performance gains of nearly 300% and 100%, respectively.

This thesis submitted to the department of Computer Science and Engineering of the faculty of the Engineering and Technology in the University of Dhaka for partial ful llment of the requirements of the degree of Doctor of Philosophy.

This paper presents a two-membered evolution strategy based approach to address the total rotation minimization problem (TRMP) pertaining to directional sensor networks. TRMP is an NP$\mathcal {N}\mathcal {P}$-hard problem. Performance of the proposed approach is enhanced by employing a pre-processing step that utilizes a constructive heuristic and the concept of opposite solutions. We have compared our approach with the best approach available in the literature. The experimental results demonstrate our approach to be highly effective with substantial gain in terms of solution quality, in comparison to the best approach available in the literature. However, our approach requires more time in comparison to this approach.

This paper addresses two versions of a lifetime maximization problem for target coverage with wireless directional sensor networks. The sensors used in these networks have a maximum sensing range and a limited sensing angle. In the first problem version, predefined sensing directions are assumed to be given, whereas sensing directions can be freely devised in the second problem version. In that case, a polynomial-time algorithm is provided for building sensing directions that allow to maximize the network lifetime. A column generation algorithm is proposed for both problem versions, the subproblem being addressed with a hybrid approach based on a genetic algorithm, and an integer linear programming formulation. Numerical results show that addressing the second problem version allows for significant improvements in terms of network lifetime while the computational effort is comparable for both problem versions.

This paper addresses the problem of maximizing lifetime of directional wireless sensor networks, i.e., where sensors can monitor targets in an angular sector only and not all the targets around them. These sectors usually do not overlap, and each sensor can monitor at most one sector at a time. An exact method is proposed using a column generation scheme where a two level strategy, consisting of a genetic algorithm and an integer linear programming approach, is used to solve the auxiliary problem. The role of integer linear programming (ILP) approach is limited to either escaping from local optima or proving the optimality of the current solution. Computational results clearly show the advantage of the proposed approach over a column generation approach based on solving the auxiliary problem through ILP approach alone as the proposed approach is several times faster.

This article addresses the problem of scheduling a set of groups of directional sensors arising as a result of applying an exact or a heuristic approach for solving a problem involving directional sensors. The problem seeks a schedule for these groups that minimizes the total energy consumed in switching from one group to the next group in the schedule. In practice, when switching from a group to the next one, active sensors in the new group have to rotate in order to face their working direction. These rotations consume energy, and the problem is to schedule the groups so as to minimize the total amount of energy consumed by all the sensor rotations, knowing the initial angular positions of all the sensors. In this article, it is assumed that energy consumption is proportional to the angular movement for all the sensors. Another problem version is also investigated that seeks to minimize the total time during which the sensor network cannot cover all the targets because active sensors are rotating. Both problems are proved to be -hard, and a lower bound for the first problem is presented. A greedy heuristic and a genetic algorithm are also proposed for addressing the problem of minimizing total rotation in the general case. Finally, a local search is also proposed to improve the solutions obtained through a genetic algorithm.

The technology of harvesting energy from the natural environment can be used to overcome the energy limitation of wireless sensor networks. In this paper, we consider the problem of deploying energy-harvesting directional sensor networks for optimal target coverage. It involves the directional sensing coverage, communication route selection, and energy neutral operation. We formulate it as a mixed integer linear programming model, and propose three heuristics to solve it, i.e., a linear program-based heuristic (LPBH), a two-stage heuristic (TSH), and a sensing- and routing-integrated greedy heuristic (SRIGH). Their approximation upper bounds and time complexities are analyzed. Finally, we conduct extensive simulation experiments to evaluate and compare them. Simulation results show that TSH is the fastest one among them, but achieves the lowest success rate and solution quality. LPBH and SRIGH can achieve roughly equal success rate and solution quality, and LPBH is the most time-consuming.

The service-oriented architecture is considered as a new emerging trend for the future of wireless sensor networks in which different types of sensors can be deployed in the same area to support various service requirements. The accuracy of the sensed data is one of the key criterions because it is generally a noisy version of the physical phenomenon. In this paper, we study the node selection problem with data accuracy guarantee in service-oriented wireless sensor networks. We exploit the spatial correlation between the service data and aim at selecting minimum number of nodes to provide services with data accuracy guaranteed. Firstly, we have formulated this problem into an integer nonlinear programming problem to illustrate its NP-hard property. Secondarily, we have proposed two heuristic algorithms, namely, Separate Selection Algorithm (SSA) and Combined Selection Algorithm (CSA). The SSA is designed to select nodes for each service in a separate way, and the CSA is designed to select nodes according to their contribution increment. Finally, we compare the performance of the proposed algorithms with extended simulations. The results show that CSA has better performance compared with SSA.

The problem of maximizing the lifetime of a wireless sensor network which uses video cameras to monitor targets is considered. These video cameras can rotate and have a fixed monitoring angle. For a target to be covered by a video camera mounted on a sensor node, three conditions must be satisfied. First, the distance between the sensor and the target should be less than the sensing range. Second, the direction of the camera sensor should face the target, and third, the focus of the video camera should be such that the picture of the target is sharp. Basic elements on optics are recalled, then some properties are shown to efficiently address the problem of setting the direction and focal distance of a video camera for target coverage. Then, a column generation algorithm based on these properties is proposed for solving three lifetime maximization problems. Targets are considered as points in the first problem, they are considered as discs in the second problem (which allows for considering occlusion) and in the last problem, focal distance is also dealt with for taking image sharpness into account. All of these problems are compared on a testbed of 180 instances and numerical results show the effectiveness of the proposed approach.